Abstract
Photonic computing plays a significant role in high-performance computing applications. The high speed and capacity of processing larger information by photonic signals assist the high-performance computing applications such as hardware accelerators, machine learning application and deep learning applications. In this work, we propose a photonic MAC (PMAC) based on reconfigurable photonic components such as reconfigurable Mach–Zehnder interferometer (RMZI), reconfigurable directional coupler (RDC) and reconfigurable micro-ring resonator (RMRR). Theoretical analysis and simulations are carried out based on MATLAB R2023a software package and Ansys Lumerical 2018a software suits. Based on the analysis it is evident that the PMAC realization, based on RDC is more suitable for MAC operations due to its smaller footprint and less sensitive (2%) to fabrication variations. Comparatively RMZI results in larger footprint and RMRR shows more sensitive (11%) to fabrication variations. The photonic MAC proposed in this work acts as the key component for machine learning and deep learning applications.
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Al-Qadasi, M.A., Chrostowski, L., Shastri, B.J., Shekhar, S.: Scaling up silicon photonic-based accelerators: challenges and opportunities. APL Photonics 7(2), 020902 (2022a)
Al-Qadasi, M.A., Chrostowski, L., Shastri, B.J., Shekhar, S.: Scaling up silicon photonic based accelerators: challenges and opportunities. APL Photonics 7(2), 020902 (2022b)
Bai, B., Shu, H., Wang, X., Zou, W.: Towards silicon photonic neural networks for artificial intelligence. Sci. China Inf. Sci. 63, 1–14 (2020)
Bogaerts, W., Fiers, M., Dumon, P.: Design challenges in silicon photonics. IEEE J. Sel. Top. Quantum Electron. 20(4), 1–8 (2013)
Chen, Y.-H., Yang, T.-J., Emer, J., Sze, V.: Eyeriss v2: A flexible accelerator for emerging deep neural networks on mobile devices. IEEE J Emerg Sel Top Circuits Syst (JETCAS) 9(2), 292–308 (2019)
De Marinis, L., Catania, A., Castoldi, P., Contestabile, G., Bruschi, P., Piotto, M., Andriolli, N.: A codesigned integrated photonic electronic neuron. IEEE J. Quant. Electron. 58(5), 1–10 (2022)
Feng C, Gu J, Zhu H, Ying Z, Zhao Z, Pan DZ, Chen RT. Silicon photonic subspace neural chip for hardware-efficient deep learning. arXiv preprint arXiv:2111.06705. 2021.
Hamerly R, Sludds A, Bernstein L, Prabhu M, Roques-Carmes C, Carolan J, Yamamoto Y, Soljacicť M, Englund D, Towards large-scale photonic neural-network accelerators. In: 2019 IEEE international electron devices meeting (IEDM), 2019, pp 22.8.122.8.4.
Huang, C., Sorger, V.J., Miscuglio, M., Al-Qadasi, M., Mukherjee, A., Lampe, L., Shastri, B.J.: Prospects and applications of photonic neural networks. Adv. Phys. X 7(1), 1981155 (2022)
Levinson, J., Askeland, J., Becker, J., Dolson, J., Held, D., Kammel, S., Thrun, S. (2011). Towards fully autonomous driving: Systems and algorithms. In 2011 IEEE intelligent vehicles symposium (IV) (pp. 163–168). IEEE
Liu, S., Wang, S., Shi, W., Liu, H., Li, Z., Mao, T.: Vehicle tracking by detection in UAV aerial video. Sci. China Inf. Sci. 62, 1–3 (2019)
Marquez, B.A., Filipovich, M.J., Howard, E.R., Bangari, V., Guo, Z., Morison, H.D., De Lima, T.F., Tait, A.N., Prucnal, P.R., Shastri, B.J.: Silicon photonics for artificial intelligence applications. Photoniques 104, 40–44 (2020)
Meerasha, M.A., Ganesh, M., Pandiyan, K.: Reconfigurable quantum photonic convolutional neural network layer utilizing photonic gate and teleportation mechanism. Opt. Quant. Electron 54, 770 (2022). https://doi.org/10.1007/s11082-022-04168-8
Mourgias-Alexandris, G., Moralis-Pegios, M., Tsakyridis, A., Simos, S., Dabos, G., Totovic, A., Passalis, N., Kirtas, M., Rutirawut, T., Gardes, F.Y., Tefas, A.: Noise-resilient and high-speed deep learning with coherent silicon photonics. Nat. Commun. 13, 1–7 (2022)
Mubarak Ali, M., Madhupriya, G., Indhumathi, R., Krishnamoorthy, P.: Photonic Processing Core for Reconfigurable Electronic-Photonic Integrated Circuit. In: Arunachalam, V., Sivasankaran, K. (eds.) Microelectronic Devices Circuits and Systems. ICMDCS 2021. Communications in Computer and nformation Science. Springer (2021)
Nahmias, M.A., De Lima, T.F., Tait, A.N., Peng, H.T., Shastri, B.J., Prucnal, P.R.: Photonic multiply-accumulate operations for neural networks. IEEE J. Sel. Top. Quantum Electron. 26(1), 1–18 (2019)
Ohno, S., Tang, R., Toprasertpong, K., Takagi, S., Takenaka, M.: Si microring resonator crossbar array for on-chip inference and training of the optical neural network. ACS Photonics 9, 2614–2622 (2022)
Paolini, E., De Marinis, L., Cococcioni, M., Valcarenghi, L., Maggiani, L., Andriolli, N.: Photonic-aware neural networks. Neural Comput. Appl. 34(18), 15589–15601 (2022)
Shaheen, S.A., Taya, S.A.: Propagation of p-polarized light in photonic crystal for sensor application. Chin. J. Phys. 55, 571–582 (2017)
Stark, P., Horst, F., Dangel, R., Weiss, J., Offrein, B.J.: Opportunities for integrated photonic neural networks. Nanophotonics 9(13), 4221–4232 (2020)
Sunny FP, Mirza A, Nikdast M. High-Performance Deep Learning Acceleration with Silicon Photonics. In Silicon Photonics for High-Performance Computing and Beyond 2021 Nov 16 (pp. 367–382). CRC Press.
Sunny, F.P., et al.: A survey on silicon photonics for deep learning. ACM J. Emerg. Technol. Comput. Syst. 17(4), 1–57 (2021)
Tait, A.N., De Lima, T.F., Nahmias, M.A., Miller, H.B., Peng, H.T., Shastri, B.J., Prucnal, P.R.: Silicon photonic modulator neuron. Phys. Rev. Appl. 11(6), 064043 (2019)
Taya, S.A.: Ternary photonic crystal with left-handed material layer for refractometric application. Opto-Electron. Rev. 26, 236–241 (2018)
Taya, S.A., Shaheen, S.A.: Binary photonic crystal for refractometric applications (TE case). Indian J. Phys. 92, 519–527 (2018)
Taya, S.A., Doghmosh, N., Upadhyay, A.: Properties of defect modes and band gaps of mirror symmetric metal-dielectric 1D photonic crystals. Opt. Quant. Electron. 53, 1–11 (2021a)
Taya, S.A., Doghmosh, N., Abutailkh, M.A., Upadhyay, A., Nassar, Z.M., Colak, I.: Properties of band gap for p-polarized wave propagating in a binary superconductor-dielectric photonic crystal. Optik 243, 167505 (2021b)
Waldrop, M.M.: The chips are down for Moore’s law. Nature News 530(7589), 144 (2016)
Xu, B., Huang, Y., Fang, Y., Wang, Z., Yu, S., Xu, R.: Recent progress of neuromorphic computing based on silicon photonics: electronic photonic co-design, device, and architecture. InPhotonics 9(10), 698 (2022)
Zhou, H., Dong, J., Cheng, J., Dong, W., Huang, C., Shen, Y., Zhang, Q., Gu, M., Qian, C., Chen, H., Ruan, Z.: Photonic matrix multiplication lights up photonic accelerator and beyond. Light Sci. Appl. 11, 1–21 (2022)
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Concept - MA; Design - MA; Supervision - JP; Resources – MA, Data Collection and/or Processing – MA; Literature Search - MA; Writing Manuscript – MA, JP; Critical Review – JP; Approvals – JP
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Mosses, A., Prathap, P.M.J. Design and analysis of on-chip reconfigurable photonic components for photonic multiply and accumulate operation. Opt Quant Electron 55, 934 (2023). https://doi.org/10.1007/s11082-023-05200-1
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DOI: https://doi.org/10.1007/s11082-023-05200-1